Relevance image sampling from collection using importance selection on randomized optimum-path trees

Autor(es)

Ponti, Moacir A. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2017 6TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS); v. N/A, p. 6-pg., 2017-01-01.

Resumo

The growth in image collections became an important issue when designing a successful image retrieval and recognition system. While it is important to investigate methods that uses smaller training sets or under samples the data, it is also challenging to be successful with a single model trained with a reduced number of samples, since they often require representative and sufficient observations to be accurate. We propose an algorithm that selects relevant images from a collection, based on pasting of small votes ensembles of optimum-path forest base classifiers. Since small training sets are used, it is viable for large datasets. Also, the classifiers tested maintained in general their performances after sampling using our method, even using significantly less training data. (AU)

• Processo FAPESP: 16/16111-4 - Aprendizado de características na recuperação de imagens baseada em rascunhos e no sensoriamento remoto de baixa altitude
• Beneficiário: Moacir Antonelli Ponti
• Modalidade de apoio: Auxílio à Pesquisa - Regular